1 Executive Summary

  • Samples 101-108 (1w SCI&Naive) + 111 (2w SCI) have double peaks in density of log-cpm plots. 101-108 had low alignment rates and 111 is a definite outlier in the MDS. Samples 101-108 and 111 are therefore removed.

2 Project Description

Hypothesis: The transcriptome of subventricular zone derived neural progenitor cells (NPCs) transplanted into a spinal cord injury environment is altered as compared to NPCs transplanted into uninjured spinal cord.

Experimental setup & sequencing: GFP+NPCs were transplanted into injured (250 kdyn contusion injury, 8-10 days post SCI) or uninjured spinal cord using a glas capillary pipette. Lewis female rats were used in all experiments. At 2 & 3 w post transplantation the GFP+NPCs were collected using FACS. FACS of GFP+NPCs from injured spinal cord was performed using injured spinal cord as reference (i.e. negative gate setting). FACS of GFP+NPCs from uninjured spinal cord was performed using uninjured spinal cord as reference. Total RNA, digested of DNase, was isolated and libraries were prepared using SMARTer Stranded Total RNA-Seq Kit - Pico Input Mammalian (Takara). Sequencing was performed 2x125bp in two lane using the HiSeq2500 system and v4 sequencing chemistry (Illumina Inc.) performed by the SNP&SEQ Technology Platform (Stockholm, Sweden).

Data analysis: Data was analysed using edgeR and limma packages, both available through Bioconductor using R (version 3.4.4, Someone to Lean On). Two additional key packages used were ggplot2 and data.table.

3 Data Overview

Table 1. Data set characteristics.

Characteristic Value
Samples (n): 19
Groups (n): 5
Unique ENSEMBL IDs (n): 32545

4 Data Pre-Processing

4.1 Removing genes with low expression

Fig 1. Density of log-CPM values pre -and post filtering

Fig 1. Figure reports the density of log-CPM for every sample (by color) pre -and post filtering of genes with low expression. The raw read count matrix is filtered based on log-CPM values. Vertical dashed line represents the cut-off (log-CPM=0, CPM=1). The figure shows a distinct shift of the density from below the threshold (Fig 1A) to above the threshold (Fig 1B). Approximately 1/2 of the genes remain post filtering.

4.2 Adjusting gene expression distributions

Fig 2. Distribution of log-CPM values pre -and post normalization

Fig 2. Figure reports the distribution of gene expression (log-CPM) for each sample. Fig 2A reports the distribution prior to normalization while Fig 2B reports the distribution following normalization of library sizes using the TMM algorithm. Boxplots are based on all log-CPM values while points represent a random sample of 1e4 genes (due to processing time issues). The difference in the distribution of log-CPM using original and effective library sizes is minor but adjusted for.

5 Dimensionality Reduction

Fig 3. Variance explained by principal components based on the 500 genes with highest variance

Fig 3. Figure reports the proportion variance explained by each principal component. Fig 3A reports the proportional variance explained by each component while Fig 3B reports the cumulative variance explained by the components.

Table 2. Upper and lower bounds (bootstrapped 95 % confidence intervals) for the proportion of variance explained by principal component 1 to 10

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
Upper bound: 0.674 0.144 0.031 0.020 0.010 0.009 0.008 0.007 0.007 0.006
Lower bound: 0.714 0.177 0.040 0.026 0.013 0.012 0.011 0.008 0.009 0.007

Fig 4. Dimensionality reduction using PCA and t-SNE of the 500 genes with highest variance

Fig 4. Figure reports the samples in low dimensional space following dimensionality reduction with PCA and t-SNE for the 500 genes with the highest variance. Ellipses are added to the samples for easier recognition of the study groups.

6 Clustering Algorithms

Fig 5. Hierarchical clustering of samples using 500 most variable genes

Fig 5. Figure reports hierarchical clustering based on the 500 most variable genes. Fig 5A reports the clustering using a dendrogram while Fig 5B reports the same clustering using a circular packing plot.

Fig 6. Clustering of samples using common methods following dimensionality reduction with PCA and t-SNE

Fig 6. Figure reports result of K-means clsutering and affinity propagation algorithms implemented on samples following dimensionality reduction using PCA and t-SNE. In the case train-test split was required a 50:50 ratio (9:9 samples) was used instead of a traditional 80:20 ratio due to low number of samples in the test set.

Fig 7. Top ten loadings for the first two principal components

Fig 7. Figure reports the top 10 positive and negative loadings for the first -and second principal component.

7 Linear Modelling and Empirical Bayes Moderation Using Precision Weights

Fig 8. Mean-variance relationship pre -and post voom transformation

Fig 8. Figure reports the mean-variance relationship pre -and post application of the voom function. Fig 8A reports the average log-CPM against the quarter root of the variance. Fig 8B reports average log-CPM against the \(log_2(st.dev)\). Blue line reports the average \(log_2(st.dev)\). The red line is a linear trend fitted to the black dots. Each black dot represents a gene. Fig 8A illustrates that the variance is decreasing when the average expression is increasing. In Fig 8B the dependency is removed and the mean variance is unchanged when the average expression increases.

Fig 9. Hierarchical clustering and circular packing plot of 1000 genes with highest F-value

Fig 9. Figure reports hierarchical clustering of samples based on the 1000 genes with highest F-values. Fig 9A reports a dendrogram while Fig 9B reports a circular packing plot.

8 Differential Gene Expression

Fig 10. Number of differentially expressed genes for each contrast (FDR<0.05)

Fig 10. Figure 10A reports the number of DE genes between contrasts Naive[2w] vs invitro, SCI[2w] vs invitro, Naive[2w] vs SCI[2w]. Figure 10B reports the number of DE genes between contrasts Naive[3w] vs invitro, SCI[3w] vs invitro and Naive[3w] vs SCI[3w].

Fig 11. Number of differentially expressed genes for each contrast (FDR<0.05)

Fig 11. Figure reports the number of DE genes within and between contrasts Naive[2w] vs SCI[2w] and Naive[3w] vs SCI[3w].

Table 3. Number of differentially over -and under-expressed genes for each contrast (FDR<0.05)

Invitro vs Naive[2w] Invitro vs Naive[3w] Invitro vs SCI[2w] Invitro vs SCI[3w]
Downregulated: 4862 4852 2411 3763
No change 5236 5733 9952 7574
Upregulated: 5013 4526 2748 3774
Sum: 15111 15111 15111 15111
Naive[2w] vs Naive[3w] Naive[2w] vs SCI[2w] Naive[2w] vs SCI[3w] Naive[3w] vs SCI[2w] Naive[3w] vs SCI[3w] SCI[2w] vs SCI[3w]
Downregulated: 677 4401 3685 3980 3203 2952
No change 13994 6009 7734 6441 8666 9876
Upregulated: 440 4701 3692 4690 3242 2283
Sum: 15111 15111 15111 15111 15111 15111

Fig 12. Mean difference -and volcano plot (FDR<0.05)

Fig 12. Left figure reports a mean-difference plot which illustrates the number of over -and under expressed genes. Threshold is set at \(log_2(fold change)\) +/-1 (blue lines). Blue dots represents genes above or below the log-fold change thresholds while red dots represent those genes which are above/below the thresholds and are significantly (p<0.05) differentially expressed. Right figure is a volcano plot which reports the number of significantly (p<0.05) over -and underexpressed genes (marked with red). Blue dots represent genes which have logFC <-1 or >1 but are not significantly expressed. Figure A&B are for contrast Naive[2w] vs SCI[2w], Figure C&D are for contrast Naive[3w] vs SCI[3w], figure E&F are for contrast Naive[2w] vs Naive[3w] and figure G&H for contrast SCI[2w] vs SCI[3w].

Table 4. 10 most significantly up -and downregulated differentially expressed genes (FDR<0.05)

4A. Contrast: Naive[2w] vs SCI[2w]

Gene log2(fold change) P-value (adjusted) Gene log2(fold change) P-value (adjusted)
Hapln2 8.19 1.6e-15 Gpr37l1 -5.12 3.8e-14
Ptgds 8.07 8.4e-15 Fads2 -5.12 3.8e-14
Gpr37 6.26 8.4e-15 Tubb2b -4.96 8.5e-14
Mal 9.72 3.8e-14 Limd1 -4.04 8.5e-14
Ldlrap1 6.68 3.8e-14 Spry2 -3.47 1.1e-13
Opalin 9.77 4.5e-14 Gadd45g -3.89 1.4e-13
Tmem63a 4.23 8.1e-14 Gpm6a -6.44 1.5e-13
Sept4 5.54 8.5e-14 Gpr17 -7.71 2.4e-13
Ndrg1 5.36 8.5e-14 Cdh2 -4.37 2.9e-13
Dse 3.82 1.1e-13 Pmel -6.35 2.9e-13

4B. Contrast: Naive[3w] vs SCI[3w]

Gene log2(fold change) P-value (adjusted) Gene log2(fold change) P-value (adjusted)
Hcn2 5.07 1.5e-13 Gadd45g -4.86 5.6e-15
Gpr37 4.97 1.8e-13 Limd1 -3.51 6.3e-13
Tmem229a 4.86 6.3e-13 Spry2 -3.05 6.3e-13
LOC361016 5.40 6.3e-13 Mdm2 -3.44 1.0e-12
Nkd1 4.42 6.3e-13 Slc35e3 -3.16 1.6e-12
Prex2 6.20 6.3e-13 Tgfb2 -5.93 3.0e-12
Fbln2 6.66 6.3e-13 Pfkfb3 -2.72 7.2e-12
Hapln2 5.07 7.8e-13 Shmt2 -3.40 1.2e-11
Ptgds 5.30 1.6e-12 Os9 -2.56 1.7e-11
Tubb4a 4.23 1.6e-12 Lrig3 -2.42 3.2e-11

4C. Contrast: Naive[2w] vs Naive[3w]

Gene log2(fold change) P-value (adjusted) Gene log2(fold change) P-value (adjusted)
Chordc1 1.02 1.3e-03 Cdh13 -2.31 5.4e-07
LOC102546648 1.39 1.4e-03 Flrt1 -3.37 3.1e-05
Ets1 1.15 2.3e-03 Rgs7 -3.57 3.1e-05
Zfp217 1.29 2.3e-03 Gpr37l1 -1.48 2.6e-04
Gprasp2 1.20 2.5e-03 Gpr17 -2.59 2.6e-04
Atp13a5 2.82 2.6e-03 Fnd3c2 -3.79 3.3e-04
Myc 1.09 3.7e-03 Otof -3.93 3.3e-04
P4ha1 1.01 4.3e-03 Gria4 -1.91 5.8e-04
LOC679811 1.12 4.3e-03 Scn1b -1.72 7.5e-04
Fam46a 1.13 4.4e-03 Nlgn3 -1.54 7.5e-04

4D. Contrast: SCI[2w] vs SCI[3w]

Gene log2(fold change) P-value (adjusted) Gene log2(fold change) P-value (adjusted)
Fbln2 6.05 5.2e-11 Cmklr1 -3.72 5.2e-11
Dhcr24 3.48 2.7e-10 Dse -3.08 5.2e-11
Fdft1 2.72 3.8e-10 Eng -3.97 5.2e-11
Hmgcs1 2.99 3.8e-10 Pgghg -3.90 1.1e-10
Fbn2 4.66 8.8e-10 Plin2 -3.58 1.1e-10
Aacs 2.16 1.4e-09 Nt5e -3.58 1.5e-10
Cyp51 2.27 1.6e-09 Acsf2 -3.07 1.6e-10
Acat2 3.21 1.8e-09 ENSRNOG00000046171 -3.58 1.7e-10
Fdps 2.20 3.4e-09 Plxdc2 -3.13 1.9e-10
Epn2 1.95 4.6e-09 Lcp1 -3.28 1.9e-10

9 Gene Ontology and KEGG Enrichment Analysis

Table 5. GO terms and KEGG pathways (FDR<0.05)

5A. Contrast: Naive[2w] vs SCI[2w]

Term Ont N Up Down P.Up P.Down
mitochondrial protein complex CC 128 6 95 1.0000000 0
respiratory chain CC 77 2 66 1.0000000 0
respiratory chain complex CC 70 1 61 1.0000000 0
inner mitochondrial membrane protein complex CC 106 3 81 1.0000000 0
mitochondrial respiratory chain CC 69 1 59 1.0000000 0
macromolecular complex CC 3903 960 1413 1.0000000 0
synapse organization BP 224 40 134 0.9999997 0
mitochondrial membrane part CC 177 16 111 1.0000000 0
nervous system development BP 1914 589 744 0.9442954 0
mitochondrial respiratory chain complex I CC 42 0 39 1.0000000 0
Pathway N Up Down P.Up P.Down
Oxidative phosphorylation 117 3 89 1.0000000 0.0e+00
Parkinson’s disease 125 14 92 1.0000000 0.0e+00
Thermogenesis 201 25 118 1.0000000 0.0e+00
Huntington’s disease 164 27 98 0.9999999 0.0e+00
Alzheimer’s disease 155 30 89 0.9999874 0.0e+00
Retrograde endocannabinoid signaling 121 22 71 0.9999735 0.0e+00
Proteasome 41 3 31 0.9999917 0.0e+00
Non-alcoholic fatty liver disease (NAFLD) 133 36 73 0.9686994 0.0e+00
Cardiac muscle contraction 58 9 37 0.9996022 3.0e-07
RNA transport 143 10 71 1.0000000 2.1e-06

5B. Contrast: Naive[3w] vs SCI[3w]

Term Ont N Up Down P.Up P.Down
RNA binding MF 1372 120 493 1 0
cytosolic ribosome CC 113 2 75 1 0
structural constituent of ribosome MF 173 1 94 1 0
cytosolic large ribosomal subunit CC 57 0 45 1 0
ribosomal subunit CC 176 3 93 1 0
translation BP 518 39 204 1 0
intracellular ribonucleoprotein complex CC 709 39 260 1 0
ribonucleoprotein complex CC 710 39 260 1 0
peptide biosynthetic process BP 532 42 205 1 0
macromolecular complex CC 3903 610 1078 1 0
Pathway N Up Down P.Up P.Down
Ribosome 136 1 82 1.0000000 0.0000000
Oxidative phosphorylation 117 3 51 1.0000000 0.0000207
Parkinson’s disease 125 12 53 0.9998333 0.0000366
Proteasome 41 0 23 1.0000000 0.0000369
Cell cycle 117 6 50 0.9999998 0.0000473
Spliceosome 123 10 49 0.9999764 0.0004380
Cardiac muscle contraction 58 7 27 0.9748786 0.0005232
RNA transport 143 4 54 1.0000000 0.0010519
Central carbon metabolism in cancer 55 10 25 0.7524314 0.0012824
EGFR tyrosine kinase inhibitor resistance 75 9 31 0.9863740 0.0024128

5C. Contrast: Naive[2w] vs Naive[3w]

Term Ont N Up Down P.Up P.Down
mitochondrial membrane part CC 177 1 36 0.9972379 0
mitochondrial envelope CC 530 7 66 0.9986879 0
inner mitochondrial membrane protein complex CC 106 0 27 1.0000000 0
mitochondrial part CC 707 11 77 0.9986088 0
mitochondrial membrane CC 496 7 61 0.9971251 0
mitochondrial protein complex CC 128 0 28 1.0000000 0
mitochondrial respiratory chain CC 69 0 20 1.0000000 0
neuron part CC 1270 38 110 0.7321345 0
respiratory chain complex CC 70 0 20 1.0000000 0
respiratory chain CC 77 0 20 1.0000000 0
Pathway N Up Down P.Up P.Down
Oxidative phosphorylation 117 0 30 1.0000000 0.0e+00
Parkinson’s disease 125 1 26 0.9903783 0.0e+00
Thermogenesis 201 4 33 0.9371245 0.0e+00
Alzheimer’s disease 155 3 28 0.9235928 0.0e+00
Huntington’s disease 164 2 27 0.9838149 0.0e+00
Non-alcoholic fatty liver disease (NAFLD) 133 3 22 0.8649308 6.0e-07
Metabolic pathways 976 17 79 0.9999417 3.1e-06
Ribosome 136 0 21 1.0000000 3.2e-06
Cardiac muscle contraction 58 0 13 1.0000000 4.0e-06
Retrograde endocannabinoid signaling 121 3 19 0.8185828 7.4e-06

5D. Contrast: SCI[2w] vs SCI[3w]

Term Ont N Up Down P.Up P.Down
immune system process BP 1494 162 633 1.0000000 0
immune response BP 775 75 394 0.9999999 0
regulation of immune system process BP 818 80 379 1.0000000 0
defense response BP 806 85 372 0.9999979 0
positive regulation of immune system process BP 570 38 290 1.0000000 0
response to stimulus BP 5466 901 1488 0.0839287 0
response to external biotic stimulus BP 576 47 274 1.0000000 0
response to other organism BP 576 47 274 1.0000000 0
innate immune response BP 366 33 201 0.9999701 0
regulation of immune response BP 436 36 225 0.9999995 0
Pathway N Up Down P.Up P.Down
Osteoclast differentiation 108 8 70 0.9951665 0
NOD-like receptor signaling pathway 132 5 79 0.9999950 0
Lysosome 107 4 65 0.9999711 0
Staphylococcus aureus infection 29 1 26 0.9914052 0
Tuberculosis 132 8 72 0.9996545 0
Leishmaniasis 53 2 38 0.9982723 0
Inflammatory bowel disease (IBD) 38 1 30 0.9980476 0
NF-kappa B signaling pathway 77 5 48 0.9939415 0
Antigen processing and presentation 54 2 37 0.9985103 0
Ribosome 136 0 70 1.0000000 0

10 Agglomerative hierarchical clustering with heatmap

Fig 13. Hierarchical clustering of samples together with heatmap of significantly differentially expressed genes

13A. Contrast: Naive[2w] vs SCI[2w] (FDR<1e-6)

13B. Contrast: Naive[3w] vs SCI[3w] (FDR<1e-6)

13C. Contrast: Naive[2w] vs Naive[3w] (FDR<0.05)

13D. Contrast: SCI[2w] vs SCI[3w] (FDR<1e-6)

Fig 13. Figure reports a heatmap with hierarchical clustering (indicated with dendrograms) using log-CPM values. Only significantly differentially expressed genes are included (genes with NA symbols were removed).

11 Gene Set Testing

11.1 Molecular Signatures Database

12 Summary

1. Annotation: Genes are annotated with gene name using their respective ENSEMBL ID.
2. Transformation: Read count matrix is transformed into log-CPM using original library sizes.
3. Filtering: Read count matrix is filtered using log-CPM values (>0 for at least 3 samples).
4. Normalization: Effective library sizes are calculated using the library sizes for the filtered read count matrix and the trimmed mean of M values (TMM) approach.
5. Transformation: Filtered read count matrix is transformed into log-CPM matrix.
6. PCA: Conducted for the 500 genes with highest variance. Proportional variance explained, MDS and loading plots are created.
7. Design matrix: A dummy matrix which indicates which group each sample belongs.
8. Contrast matrix: Contrasts are the group comparisons of interest.
9. Voom transformation: Estimate precision weights for linear modelling to remove dependency between the variance and trhe mean.
10. Linear modelling: Linear modelling using precision weights followed by an empirical Bayes moderation.
11. Differentially expressed genes: Moderated t-statistics are used for determining significantly expressed genes for each contrast. Results are displayed with venn diagrams, mean-difference -and volcano plot and a summary table.
12. Analysis/interpretation: Using hierarchical clustering, heatmap, gene ontology and KEGG enrichment analysis and gene set analysis the difference between the study groups is sought for.

13 Bibliography

[1] R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL htts://www.R-project.

[2] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

[3] Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140

[4] Law CW, Alhamdoosh M, Su S et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 1; referees: 3 approved]. F1000Research 2016, 5:1408.

14 Setup

This analysis was conducted on:

sessionInfo()
## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=sv_SE.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=sv_SE.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=sv_SE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] circlize_0.4.3                         
##  [2] ape_5.0                                
##  [3] igraph_1.2.1                           
##  [4] ggraph_1.0.1                           
##  [5] e1071_1.6-8                            
##  [6] apcluster_1.4.5                        
##  [7] MeanShift_1.1-1                        
##  [8] wavethresh_4.6.8                       
##  [9] EMCluster_0.2-10                       
## [10] Matrix_1.2-11                          
## [11] kernlab_0.9-25                         
## [12] mda_0.4-10                             
## [13] class_7.3-14                           
## [14] MASS_7.3-47                            
## [15] Rtsne_0.13                             
## [16] caret_6.0-79                           
## [17] lattice_0.20-35                        
## [18] xml2_1.2.0                             
## [19] XML_3.98-1.3                           
## [20] RCurl_1.95-4.10                        
## [21] bitops_1.0-6                           
## [22] pander_0.6.1                           
## [23] knitr_1.20                             
## [24] pvclust_2.0-0                          
## [25] boot_1.3-20                            
## [26] rafalib_1.0.0                          
## [27] ggrepel_0.7.0                          
## [28] plot3D_1.1.1                           
## [29] ellipse_0.4.1                          
## [30] VennDiagram_1.6.20                     
## [31] futile.logger_1.4.3                    
## [32] gplots_3.0.1                           
## [33] RColorBrewer_1.1-2                     
## [34] colorspace_1.3-2                       
## [35] gridExtra_2.3                          
## [36] cowplot_0.9.2                          
## [37] ggplot2_2.2.1.9000                     
## [38] data.table_1.10.4-3                    
## [39] Rattus.norvegicus_1.3.1                
## [40] TxDb.Rnorvegicus.UCSC.rn5.refGene_3.4.1
## [41] org.Rn.eg.db_3.4.1                     
## [42] GO.db_3.4.1                            
## [43] OrganismDbi_1.18.1                     
## [44] GenomicFeatures_1.28.5                 
## [45] GenomicRanges_1.28.6                   
## [46] GenomeInfoDb_1.12.3                    
## [47] AnnotationDbi_1.38.2                   
## [48] IRanges_2.10.5                         
## [49] S4Vectors_0.14.7                       
## [50] Biobase_2.36.2                         
## [51] BiocGenerics_0.22.1                    
## [52] edgeR_3.18.1                           
## [53] limma_3.32.10                          
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.2            plyr_1.8.4                
##   [3] lazyeval_0.2.1             splines_3.4.4             
##   [5] BiocParallel_1.10.1        digest_0.6.15             
##   [7] foreach_1.4.4              BiocInstaller_1.26.1      
##   [9] htmltools_0.3.6            viridis_0.5.1             
##  [11] gdata_2.18.0               magrittr_1.5              
##  [13] memoise_1.1.0              sfsmisc_1.1-2             
##  [15] recipes_0.1.2              Biostrings_2.44.2         
##  [17] gower_0.1.2                dimRed_0.1.0              
##  [19] matrixStats_0.53.1         blob_1.1.1                
##  [21] dplyr_0.7.4                graph_1.54.0              
##  [23] bindr_0.1.1                survival_2.41-3           
##  [25] iterators_1.0.9            glue_1.2.0                
##  [27] DRR_0.0.3                  gtable_0.2.0              
##  [29] ipred_0.9-6                zlibbioc_1.22.0           
##  [31] XVector_0.16.0             DelayedArray_0.2.7        
##  [33] ddalpha_1.3.1.1            shape_1.4.4               
##  [35] DEoptimR_1.0-8             scales_0.5.0.9000         
##  [37] futile.options_1.0.0       DBI_0.8                   
##  [39] Rcpp_0.12.16               viridisLite_0.3.0         
##  [41] units_0.5-1                foreign_0.8-69            
##  [43] bit_1.1-12                 lava_1.6.1                
##  [45] prodlim_1.6.1              pkgconfig_2.0.1           
##  [47] nnet_7.3-12                locfit_1.5-9.1            
##  [49] labeling_0.3               tidyselect_0.2.4          
##  [51] rlang_0.2.0.9000           reshape2_1.4.3            
##  [53] munsell_0.4.3              tools_3.4.4               
##  [55] RSQLite_2.1.0              broom_0.4.4               
##  [57] evaluate_0.10.1            stringr_1.3.0             
##  [59] yaml_2.1.18                ModelMetrics_1.1.0        
##  [61] bit64_0.9-7                robustbase_0.92-8         
##  [63] caTools_1.17.1             purrr_0.2.4               
##  [65] bindrcpp_0.2.2             RBGL_1.52.0               
##  [67] nlme_3.1-131               RcppRoll_0.2.2            
##  [69] biomaRt_2.32.1             compiler_3.4.4            
##  [71] tweenr_0.1.5               tibble_1.4.2              
##  [73] stringi_1.1.7              highr_0.6                 
##  [75] psych_1.7.8                pillar_1.2.1              
##  [77] GlobalOptions_0.0.13       rtracklayer_1.36.6        
##  [79] R6_2.2.2                   KernSmooth_2.23-15        
##  [81] codetools_0.2-15           lambda.r_1.2              
##  [83] gtools_3.5.0               assertthat_0.2.0          
##  [85] CVST_0.2-1                 SummarizedExperiment_1.6.5
##  [87] rprojroot_1.3-2            withr_2.1.2               
##  [89] GenomicAlignments_1.12.2   Rsamtools_1.28.0          
##  [91] mnormt_1.5-5               GenomeInfoDbData_0.99.0   
##  [93] udunits2_0.13              rpart_4.1-11              
##  [95] timeDate_3043.102          tidyr_0.8.0               
##  [97] rmarkdown_1.9              misc3d_0.8-4              
##  [99] ggforce_0.1.1              lubridate_1.7.3